Method and system for providing a highly-personalized recommendation engine

ABSTRACT

Various embodiments of a deep learning (DL)-based face perception engine for constructing, providing, and applying a highly-personalized face perception model for an individual through a deep learning process are disclosed. In some embodiments, a disclosed face perception engine includes a deep neural network configured for training a personalized face perception model for a unique individual based on a standard set of training images and a corresponding set of decisions on the set of training images provided by the unique individual. When sufficiently trained using the standard set of training images and the corresponding set of decisions, the personalized face perception model for the unique individual perceives a new face photo/image as if through the eyes of that unique individual. Hence, the trained face perception model can be used an “agent” or “representative” of the associated person in making very personal decisions, such as to decide if a given face photo/image includes a desirable face in the eyes of that person.

PRIORITY CLAIM AND RELATED PATENT APPLICATIONS

This patent application claims benefit of priority under 35 U.S.C.119(e) to U.S. Provisional Patent Application No. 62/557,747 entitled“METHOD AND SYSTEM FOR PROVIDING A HIGHLY-PERSONALIZED RECOMMENDATIONENGINE,” by inventors Yu Huang and Fang Chen, and filed on Sep. 12, 2017(Attorney Docket No. PRE001.PRV01), the content of which is incorporatedherein by reference as a part of this patent document.

This patent application also claims benefit of priority under 35 U.S.C.119(e) to U.S. Provisional Patent Application No. 62/558,340 entitled“METHOD AND SYSTEM FOR PROVIDING A HIGHLY-PERSONALIZED RECOMMENDATIONENGINE,” by inventors Yu Huang and Fang Chen, and filed on Sep. 13, 2017(Attorney Docket No. PRE001.PRV02), the content of which is incorporatedherein by reference as a part of this patent document.

TECHNICAL FIELD

This patent document generally relates to deep learning and deeplearning-based recommendation systems, and more specifically to systems,devices, and processes for providing a highly-personalizedrecommendation engine for online dating services and mobile datingapplications based on facial recognition and deep learning technology.

BACKGROUND

Driven by the explosive growth in social networking applications andcomputing technology, online dating services and mobile datingapplications have become increasingly prevalent and have reshaped theway people look for potential dates. To use a popular online datingservice or mobile dating App, a person simply pays a fee and creates aprofile with at least one photo of him or herself to join, and theninstantly gain access to virtually an unlimited number of potentialdates. Online dating services and mobile dating applications (or “Apps”)represent a multi-billion dollar industry which is expected to continueits rapid growth.

For any given popular online dating service or mobile dating App, a userroutinely faces the task of judging through a large number of profilesthat meet the user's general descriptions for potential dates. To speedup this process for a user, some of these services and Apps allow theuser to make quick “like” or “dislike” decisions of recommended usersbased on a single profile photo of another user. For example, Tinder™smartphone App allows a user to accept or reject another user with asingle left or right swipe on the other user's profile photo. However,using a single action/gesture selection to accept or reject profilephotos can lead to a time-consuming and frustrating experience becausethe users still have to go through a large number of profile photos, butoften end up rejecting most or all of the recommended profiles.

SUMMARY

Disclosed are various embodiments of an artificial intelligence(AI)/Deep learning (DL)-based face perception engine for constructingand providing a highly-personalized face perception model for a uniqueindividual through a deep learning process. In some embodiments, adisclosed face perception engine includes a DL neural network (alsoreferred to as “deep neural network”), such as a convolution neuralnetwork (CNN) configured for training a personalized face perceptionmodel for the unique individual based on a standard set of trainingimages and a corresponding set of decisions on the set of trainingimages provided by the unique individual. When sufficiently trainedusing the standard set of training images and the corresponding set ofdecisions, the personalized face perception model for the uniqueindividual perceives a new face photo/image as if through the eyes ofthat unique individual. Hence, the trained face perception model can beused an “agent” or “representative” of the associated person in makingvery personal decisions, such as to decide if a given face photo/imageincludes a desirable face in the eyes of that person. Moreover, thetrained face perception model can be built into various applications andused across various platforms, including online dating services andmobile dating Apps, as well as content delivery services, advertisementservices, entertainment Apps, and other types of recommendation engines.

In one aspect, a process for constructing a personalized face perceptionmodel for a unique individual is disclosed. This process includes thesteps of: receiving a set of face images and a corresponding set ofdesirability scores, wherein each of the desirability scores representsa degree of desirability toward an associated face image provided by theindividual based on the individual's perception of a desirable face;providing the set of face images and the corresponding set ofdesirability scores to a deep learning (DL) neural network, wherein theDL neural network includes a set of features and a set of parametersassociated with the set of features; and training the DL neural networkusing the set of face images as inputs and the corresponding set ofdesirability scores as outputs to generate a personalized faceperception model for the unique individual, wherein the personalizedface perception model includes a trained set of parameters which isunique to the unique individual. Subsequently, the personalized faceperception model can be used to automatically infer a desirability scorefrom a new face image on behalf of the unique individual according tothe learned perception of the unique individual.

In some embodiments, prior to receiving the set of face images and thecorresponding set of desirability scores, the process generates thecorresponding set of desirability scores by: providing the set of faceimages to the unique individual; and guiding the unique individual tolabel each of the set of face images with a desirability score based onthe unique individual's inherent ability of judging a face as desirableor undesirable.

In some embodiments, the desirability score for a given face image inthe set of face images is one of a set of discrete values representingdifferent degrees of desirability toward the given face image.

In some embodiments, for a particular group of individuals, the set offace images is substantially identical for each individual in the groupof individuals, while the set of desirability scores is different fordifferent individuals in the group of individuals.

In some embodiments, the DL neural network includes a convolution neuralnetwork (CNN), and the set of features includes a set of pre-definedfilters representing a set of pre-defined facial features. Hence,training the CNN using the set of face images includes training a set ofweights associated with the set of pre-defined facial features.

In some embodiments, the set of features additionally includes a set ofunspecified filters, each of the set of unspecified filters includes aset of trainable parameters. Hence, training the CNN using the set offace images additionally includes training the set of trainableparameters in the set unspecified filters to construct the setunspecified filters for the unique individual.

In some embodiments, after generating the personalized face perceptionmodel, the process applies the personalized face perception model to alarge number of new face images to select desirable face images amongthe new face images on behalf of the unique individual with very highaccuracy, thereby preventing the unique individual from personallyscreening the large number of new face images.

In some embodiments, the process applies the personalized faceperception model to the large number of new face images by: receivingeach new face image as an input to the personalized face perceptionmodel; generating a like/dislike decision or a desirability score forthe new face image on behalf of the unique individual using the trainedDL neural network; and if the new face image is determined with a likedecision or to be desirable, providing the new face image to the uniqueindividual as a personalized recommendation.

In some embodiments, the process further includes the steps of:receiving a user decision on the recommended new face image from theunique individual; and updating the personalized face perception modelusing the new face image and the associated user decision as a part ofnew training data.

In some embodiments, the process further includes the steps of:generating a plurality of personalized face perception models for aplurality of individuals, wherein each of the plurality of personalizedface perception models corresponds to each of the plurality ofindividuals, and wherein each of the plurality of personalized faceperception models generates a desirability score for an input face imageon behalf of the corresponding individual, and wherein the desirabilityscore has a value from a set of discrete values representing differentdegrees of desirability; applying the plurality of personalized faceperception models to a given face image to generate a set ofdesirability scores for the plurality of individuals; and computing anoverall desirability score for the given face image by averaging the setof desirability scores, wherein the overall desirability score measuresan overall degree of desirability for the given face image of theplurality of individuals.

In another aspect, a personalized face perception system is disclosed.This personalized face perception system includes a face perceptionmodel training subsystem (or “model training subsystem”) which furtherincludes a deep learning (DL) neural network. This DL neural networkincludes a set of features and a set of parameters associated with theset of features. In some embodiments, the face perception model trainingsubsystem is configured to: receive a set of face images and acorresponding set of desirability scores, wherein each of thedesirability scores represents a degree of desirability toward anassociated face image provided by an individual based on theindividual's perception of a desirable face; train the DL neural networkusing the set of face images as inputs to the DL neural network and thecorresponding set of desirability scores as outputs of the DL neuralnetwork to generate a personalized face perception model for theindividual, wherein the personalized face perception model includes atrained set of parameters which is unique to the individual. Thepersonalized face perception system further includes a face imageprocessing subsystem coupled to the face perception model trainingsubsystem. In some embodiments, the face image processing subsystem isconfigured to: receive the personalized face perception model from theface perception model training subsystem; receive a set of new faceimages from an external source; and applying the personalized faceperception model to the set of new face images to select desirable faceimages among the set of new face images on behalf of the individual withvery high accuracy, thereby preventing the individual from personallyscreening the set of new face images.

In some embodiments, prior to receiving the set of face images and thecorresponding set of desirability scores, the face perception modeltraining subsystem is further configured to generate the correspondingset of desirability scores by: providing the set of face images to theunique individual; and guiding the unique individual to label each ofthe set of face images with a desirability score based on the uniqueindividual's inherent ability of judging a face as desirable orundesirable.

In some embodiments, the face image processing subsystem is configuredto apply the personalized face perception model to a large number of newface images to select desirable face images among the new face images onbehalf of the unique individual with very high accuracy, therebypreventing the unique individual from personally screening the largenumber of new face images.

In some embodiments, the face image processing subsystem applies thepersonalized face perception model to the large number of new faceimages by: receiving each new face image as an input; generating alike/dislike decision or a desirability score for the new face image onbehalf of the unique individual using the trained DL neural network; andif the new face image is determined with a like decision or to bedesirable, providing the new face image to the unique individual as apersonalized recommendation.

In some embodiments, the face perception model training subsystem isfurther configured to: receive a user decision on the recommended newface image from the unique individual; and update the personalized faceperception model using the new face image and the associated userdecision as a part of new training data.

In some embodiments, the face perception model training subsystem isfurther configured to generate a plurality of personalized faceperception models for a plurality of individuals. Each of the pluralityof personalized face perception models corresponds to each of theplurality of individuals, and each of the plurality of personalized faceperception models generates a desirability score for an input face imageon behalf of the corresponding individual, and the desirability scorehas a value from a set of discrete values representing different degreesof desirability.

In some embodiments, the face image processing subsystem is furtherconfigured to: apply the plurality of personalized face perceptionmodels to a given face image to generate a set of desirability scoresfor the plurality of individuals; and compute an overall desirabilityscore for the given face image by averaging the set of desirabilityscores, wherein the overall desirability score measures an overalldegree of desirability for the given face image of the plurality ofindividuals.

In yet one aspect, a process for constructing and using a personalizedface perception model for a unique individual is disclosed. This processincludes the steps of: receiving a set of face images and acorresponding set of desirability scores, wherein each of thedesirability scores represents a degree of desirability toward anassociated face image provided by an individual based on theindividual's perception of a desirable face; receiving a personalizedface perception model based on a deep learning (DL) neural network,wherein the DL neural network includes a set of features and a set ofparameters associated with the set of features; training thepersonalized face perception model using the set of face images asinputs to the DL neural network and the corresponding set ofdesirability scores as outputs of the DL neural network to generate atrained personalized face perception model for the individual, whereinthe trained personalized face perception model includes a trained set ofparameters which is unique to the individual; and applying the trainedpersonalized face perception model to a set of new face images to selectdesirable face images among the set of new face images on behalf of theindividual with very high accuracy, thereby preventing the individualfrom personally screening the set of new face images.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 presents a flowchart illustrating a process of constructing aface perception model for an individual in accordance with someembodiments described herein.

FIG. 2 presents a flowchart illustrating a process of processing faceimages using the constructed face perception model for the associatedindividual in accordance with some embodiments described herein.

FIG. 3 illustrates a block diagram of an exemplary personalized faceperception engine in accordance with some embodiments described herein.

FIG. 4 illustrates an example network environment which provides forimplementing the disclosed personalized face perception engine inaccordance with some embodiments described herein.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology may bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a thorough understandingof the subject technology. However, the subject technology is notlimited to the specific details set forth herein and may be practicedwithout these specific details. In some instances, structures andcomponents are shown in block diagram form in order to avoid obscuringthe concepts of the subject technology.

Throughout the specification, terms “a user”, “a person”, “anindividual” “a given user”, “a given person”, “a unique user”, ‘a uniqueperson”, and “a unique individual” are used interchangeably to mean aparticular person in the world who differentiates from another person inthe world by a unique set of physical and mental characteristicspossessed by that particular person.

People react to a facial image or face photo (terms “facial/faceimage/photo” are used interchangeably hereinafter) in an emotionalmanner. Typically, a visual encounter of a desirable human facegenerates a positive reaction, whereas an undesirable face triggers anegative reaction. The ability to define a face as either desirable orundesirable is inherent in human nature, and is highly personal. Suchdefinition is the product of millions of encounters and impressions in aperson's lifetime, which evolves with time. While it is well-known thatthe ability to define a face as desirable or undesirable exists in everyone of us, it is extremely difficult to quantify such ability. For eachindividual, the perception of “beauty” is generated by a combination ofmany tangible and intangible factors. For example, facial geometry,shapes of the eyes and noses are some of the tangible factors. Someintangible factors include a person's prior encounters and emotionalexperiences, media influences, and peer pressure. All these tangible andintangible factors within a person's brain lead to a highlyindividualized decision mechanism which is subconsciously applied toeach new encounter of that person.

Efforts are made to simulate this decision mechanism by online datingservices and Apps. One approach is to create profile models forindividuals that capture facial features based on user-provided profilephotos, and compare these profile models with attributes provided by theusers to look for matches (as in the case of Match.com). In some cases,these attributes are provided in a descriptive form, such as in verbaldescription. In some other cases, certain benchmarks are used, such asby providing photos of user's exes. However, the problem with thisapproach is that it is extremely difficult for any person to accuratelyand sufficiently describe what he or she perceives as a desirable facein a descriptive manner such as in words and/or with a limited number ofphotos.

Another approach involves using machine learning in conjunction with alarge quantity of data. Dating platform such as Tinder™ can provideservices for users to screen potential candidates. A user will gothrough candidates and make a decision on each candidate with a like ordislike action. This human process can be sped up with betterrecommendations when enough data are studied, and recommendations canbecome more accurate which generates higher conversion rate.Unfortunately, this recommendation platform is based on algorithms thattarget different categories of users rather than individual users. Sucha platform has an incentive to keep users active by constantlygenerating “good enough” recommendations rather than highly likelycandidates. In other words, this platform is more likely to provideusers with candidates at 70% compatibility without a motivation tostrive for 95% or higher compatibility.

As mentioned above, the propensity to “like” or “dislike” someone basedon facial characteristics is the result of years of interaction with andobservation of tens of thousands up to millions of faces in one'slifetime. Hence, it can be reasonably assumed that each person buildshis/her mental model for making such decisions through an activelytraining process. Empirical evidence shows that people tend to followcertain patterns when it comes to “likes” or “dislikes.” For example,the phenomenon that a person is more inclined to date someone who lookslike his/her ex indicates that people tend to look for partnership withan established set of facial features.

Consequently, it can be concluded that at any given time in a person'slifetime, the person possesses a mental model having a specific set of“features,” wherein this person consciously or subconsciously appliesthis mental model to “classify” faces in real life encounters or inimagery forms as desirable or undesirable based on the associated set offeatures. More specifically, this mental model can have the followingproperties. First, the mental model is highly personalized orindividualized, that is, at any give time there is one unique model fora given person that is different from a mental model of another person.Second, this mental model is trained and evolves with time. In otherwords, each mental model is a result of a training process particular toa given person's unique life experiences. Third, the mental model isdefined by a set of features, and the model makes decisions on each facethrough a feature matching process.

Disclosed are various embodiments of an artificial intelligence(AI)/Deep learning (DL)-based face perception engine for constructingand providing a highly-personalized face perception model for a uniqueindividual through a deep learning process. In some embodiments, adisclosed face perception engine includes a DL neural network (alsoreferred to as “deep neural network”), such as a convolution neuralnetwork (CNN) configured for training a personalized face perceptionmodel for the unique individual based on a standard set of trainingimages and a corresponding set of decisions on the set of trainingimages provided by the unique individual. Moreover, the deep neuralnetwork used to implement the disclosed face perception engine caninclude an imageNet-based deep learning framework such as VGGNet,ResNet, DenseNet, Dual Pathway Network, MobileNet or Inception v1-v3.

When sufficiently trained using the standard set of training images andthe corresponding set of decisions, the personalized face perceptionmodel for the unique individual perceives a new face photo/image as ifthrough the eyes of that unique individual. Hence, the trained faceperception model can be used an “agent” or “representative” of theassociated person in making very personal decisions, such as to decideif a given face photo/image includes a desirable face in the eyes ofthat person. Moreover, the trained face perception model can be builtinto various applications and used across various platforms, includingonline dating services and mobile dating Apps, as well as contentdelivery services, advertisement services, entertainment Apps, and othertypes of recommendation engines.

When implemented within an online dating service or a mobile dating App,a trained face perception model for a given user can be applied to alarge number of profile photos to select those desirable profile photoson behalf of the given user with very high accuracy, and subsequentlypresenting the selection results to the given user for final selection.In doing so, the user no longer needs to personally, constantly andone-by-one screen the large number of profile photos to filter out oftentimes just a few desirable profiles in a very time-consuming manner. Theproposed face perception engine ensures that the recommended profiles bythe personalized face perception model represent the choices that wouldhave been made by the given user if the user had screen the same set ofprofile photos. Moreover, the personalized face perception model can bea dynamic model which is constantly updated based on the user'sdecisions on the recommended profile photos as well as new trainingimages, whereby keeping the face perception model up to date with user'schange in preferences.

In some embodiments, the proposed personalized face perception engineincludes a face perception model training subsystem (e.g., faceperception model training subsystem 302 in FIG. 3) configured togenerate the aforementioned personalized perception model for eachunique user. More specifically, the face perception model trainingsubsystem can include a deep-learning (DL) neural network (also referredto as “deep neural network” or “DNN” hereinafter, e.g., DNN 305 in FIG.3), such as a CNN. The proposed DNN can include a set of processing (orhidden) layers, and each of the processing layers can include a set offilters (i.e., neurons). When the proposed DNN is implemented with aCNN, the set of processing layers includes a set of convolution layers,and each of the set of convolution layers includes or is composed of aset of filters and the associated weight parameters. For a givenconvolution layer, the set of associated filters can be a set of facialfeatures having a similar complexity. For example, when the disclosedface perception model is implemented with a CNN, a shallower (i.e., alower numbered) layer in the CNN can include filters for identifyingsimpler features such as edges and curves. A deeper (i.e., a highernumbered) layer in the CNN can include filters for identifying morecomplex facial features such as shapes of eyes, noses, mouths, eyebrows,etc. An even deeper layer can include filters for identifying even morecomplex features or combination of features including facial geometries,e.g., geometrical relationships among different facial objects. Notethat the above described filters/features represent different classes ofidentifiable characteristics of a human face or a combination of theidentifiable characteristics of a human face.

In some embodiments, to train the DNN within the proposed faceperception model training subsystem for a given user (i.e., a givenperson or a unique individual), the face perception model trainingsubsystem receives a set of training face images, and a correspondingset of decisions (i.e., a set of labels) on the set of training faceimages from the given user. More specifically, for each training faceimage in the set of training face images, a corresponding decision inthe set of decisions is a desirability score provided by the given userbased on that user's own perception of face desirability. In otherwords, prior to training a new face perception model for a new user, thenew user is instructed to personally label/classify each training imagein the set of training face images by making a decision on the trainingimage, i.e., providing a classification of the training image in termsof a degree of desirability. Hence, the user-provided classification ofthe training image can be referred as a “desirability score.”

For example, the desirability score for a given training face image canhave only two values/classifications: 0 being undesirable and 1 beingdesirable. As another example, the desirability score can have threevalues/classifications: 0 being undesirable; 1 being uncertain; and 2being desirable. As yet another example, the desirability score can havefour values/classifications: 0 being highly undesirable; 1 beingsomewhat undesirable; 2 being somewhat desirable; and 3 being highlydesirable. As yet another example, the desirability score can have fivevalues/classifications: 0 being highly undesirable; 1 being somewhatundesirable; 2 being uncertain; 3 being somewhat desirable; and 4 beinghighly desirable. As yet another example, the desirability score canhave ten values/classifications (i.e., the so-called “one-to-ten”rating): with 1 being highly undesirable and 10 being highly desirable,and each of the other 8 values represents a different degree/level ofdesirability which gradually increases from 1 to 10. Note that typicallythe more values/classifications the desirability score includes, themore accurately the desirability score can be used to reflect the user'sperception. However, above certain levels, it becomes difficult for auser to make a more meaningful decision for a given face image.Moreover, more levels also leads to more time spent by the user to makethe decision which can lead to negative user experience.

Hence, after the given user has made the decisions on the set oftraining images, the set of images becomes uniquely “labeled.”Subsequently, the face perception model training subsystem receives theset of labeled training images as ground truth and a DNN model trainingprocess can be initiated and carried out using the training images asinputs and the associated labels as training targets/outputs. Note thatwhile the training data may be similar or identical for different users,the training targets/outputs are different for different users becausefor each training image the training target/output includes a uniquedecision made by a given person (i.e., in the form of the desirabilityscore). Hence, the more training images are used, the more personaldecisions/training data are received and used to build each faceperception model, thereby further differentiating one face perceptionmodel from another face perception model among a large number of faceperception models constructed for a large number of people.Consequently, one key difference of the proposed personalized faceperception engine from a conventional DL-based profile recommendationsystem is that, the proposed face perception model training subsystem isused to generate a highly-personalize model for each individual bytraining the model based on the set of highly personalized decisions(i.e., personalized desirability scores) instead of generating ageneralized or semi-generalized model for a group of users based on aset of standard classifications of the group of users (such as sex, age,geographical location, etc.).

As mentioned above, the proposed face perception model trainingsubsystem can use a DNN including many layers of identifiable features.In other words, the sets of filters/features used by a proposed DNN canbe pre-defined filters/features for understanding human faces. As such,a trained face perception model for a unique individual can include setsof trained weights and biases associated with the sets of thepre-defined filters/features. However, it can be reasonably assumed thata perception/mental model of a unique individual can also includefeatures which cannot be represented by geometrical parameters andcombinations thereof. These features can be highly abstract andperson-specific. However, these features can be learned through theabove-described DNN training process in one or more deeper layers of theDNN. In some embodiments, to identify such hidden perception/mentalfeatures within a person's brain, one or more layers of the DNN can beused to include filters/features which are represented by/constructedwith trainable parameters rather then fixed values. Hence, training aface perception model for an individual can also include regressing andobtaining the values of these trainable filters/features along with theweights and biases of those pre-defined filters/features. As a result,establishing a new face perception model for a given individual caninclude extracting a set of features particular to that individual, andthe differences from one model for a user A to another model for a userB can include both different weights and biases for sets of standardizedfilters/features typically used for face recognition and differentcustom-trained and extracted filters/features.

In some embodiments, after a new face perception model has beenestablished, the set of extracted features unique to the individual canbe fixed during the time when the model is updated for that individualwith new images, and only the weights and biases of these new featuresneed to be updated. However, in some embodiments of the proposed faceperception model training subsystem, the DNN only uses pre-definefilters/features and as such the differences from one trained model fora user A to another trained model for a user B lie primarily in thedifferent weights and biases associated with these pre-definefilters/features.

While it is possible to use the same standard set of training images totrain different models for different individuals, in practice differentstandard sets of training images can be used for different groups ofusers based on one or more classifications. For example, for a male userlooking for a female match, it is not necessary to train the model ofthe male user using male profile images, and vice versa. In other words,the training image set can be a more targeted set for a particular groupof users, and the user-provided attributes can be used to narrow downthe training dataset, for example, based on sexual orientations andages. In doing so, not only users require to do much less work labelingthe training data, but the training process becomes much faster and moreaccurate. Nevertheless, in the proposed system and technique, a same setof training images can be used to establish an unlimited number ofunique face perception models for an unlimited number of people bytraining a particular DNN model, wherein each of the face perceptionmodels can be trained to become an accurate decision model of facedesirability for each unique person. In some embodiments, trained faceperception models can be updated later on by providing more trainingface images to the users. In these embodiments, the users label thenewly provided training images by making desirability decisions on theseimages, and the DNN is trained and updated based on both the previouslylabeled training images and newly labeled training images. Because usingmore training images typically generates more accurate models, inpractice, the training images can be provided to the users several timeinstead of all at once to reduce the time required to label trainingimages at a given time, particularly when the training data set islarge.

FIG. 1 presents a flowchart illustrating a process of constructing aface perception model for an individual in accordance with someembodiments described herein. In some embodiments, the process begins byproviding an individual with a set of training face images (step 102).As mentioned above, the set of training face images can be determinedfor the individual based on the attributes of the individual, so that agroup of individuals having the same or similar attributes can beprovided with the same or similar set of the training face images. Inone embodiment, the set of training face images is composed of a set ofprofile photos provided by a group of volunteers or by a group ofincentivized people. The size of the set of training face images can bein the range of 100-1000 images but can also be less than 100 images orgreater than 1000 images. For example, the set of training images caninclude 250 images.

Next, the process guides the individual to label each of the trainingface images with a desirability score based on the individual's personalperception of a desirable face (step 104). As mentioned above, thedesirability score can have two or more desirability levels, and the setof the desirability levels represent the set of classifications for theDNN. Next, the process provides the set of labeled training face imagesto a DNN face-perception model (or “DNN model”), such as a multi-stageCNN model, which includes a set of training parameters (step 106). Insome embodiments, the DNN model can include an imageNet-based deeplearning framework such as VGGNet, ResNet, DenseNet, Dual PathwayNetwork, MobileNet or Inception v1-v3.

As mentioned above, a subset of the training parameters can be includedin a subset of filters/features of the DNN model which have not beenpreviously defined, and the subset of undefined filters/features can beincluded in one or more DNN layers and subsequently fitted/determinedthrough the model training process. In some embodiments, prior toperforming step 106 based on the set of uniquely labeled training faceimages from the individual, the DNN model for the individual can bepre-trained or semi-trained before receiving. Note that the pre-trainingof the DNN model can be different for different individuals.

Next, the DNN model for the individual is trained using the set oftraining face images as inputs and the associated desirabilityscores/labels as training targets/outputs to generate a personalizedface perception model for the individual (step 108). In someembodiments, the trained/established personalized face perception modelfor the individual includes trained sets of weights and biasesassociated with sets of pre-defined facial filters/features. In someother embodiments, the trained/established personalized face perceptionmodel for the individual additionally includes new/personalizedfilters/features which were not previously-defined but trained andestablished for the individual through the model training process.

The proposed personalized face perception engine also includes a faceimage processing subsystem (e.g., face image processing subsystem 304 inFIG. 3). More specifically, once a face perception model is constructedfor an individual, the disclosed face image processing subsystem can beconfigured to apply the face perception model to real world face imageson behalf of the associated individual. More specifically, the faceperception model includes a trained-DNN having establishedfeatures/filters, weights and biases, and can be applied to a largenumber of face images to generate desirability scores or simple “like”or “dislike” decisions through a deep learning process on behalf of theindividual. For example, when implemented in an online dating service ora mobile dating App, the disclosed face image processing subsystem canuse the established DL face perception model to process profile photoswith extremely high accuracy because each model was constructedspecifically for a unique user. Next, those profile photos determined tobe desirable to the unique user can be provided to the user aspersonalized recommendations. The user can then make final selectionsamong a very manageable set of profile photos, wherein each of theprofile photo already has a very high likelihood of being selected bythe user him/herself. In some embodiments, the face perception model canbe updated/re-trained later on based on the user's decisions on therecommended set of profile photos, which can be combined withnew/additional training images as mentioned above. In this manner, thedisclosed face perception model established for a unique individual canbe dynamic, i.e., evolve over time to adapt to the individual and bekept up to date with the individual user's change inpreferences/perceptions of human faces.

Note that the disclosed face perception model training process of FIG. 1does not need to be performed and completed in a “one-off” manner forthe entirely set of training images. If the training image set is large(e.g., with 500 images), asking a new user to label all of the trainingimages all at once can cause user fatigue and can lead to poor userexperiences. In some embodiments, the training process can be performedand completed in “pieces” over a number of times over a predeterminedtime period, e.g., 5 times over a few days, 10 time over a week, or 30times over a month. For each “piece” of the training, a subset of thetraining image set (e.g., 1/10 of the image set in a 10-step trainingprocess) can be provided to the user for labeling and the model can bepartially trained based on the always labeled training images. Note thatin these staged-training embodiments, the training data is accumulativeand each time a set of new training images has been labeled, the modelcan be re-trained with these newly-labeled training images and all ofthe preciously labeled training images from the previous stages of thetraining process. However, in other embodiments, each time when a newset of images has been labeled, the partially-trained model is onlyupdated with the newly-labeled training images instead of beingre-trained with all of currently labeled training images from thecurrent and previous training stages. Note that the disclosed faceperception model can also be periodically re-trained with the sametraining data set, but combined with “new” or “updated” userdecisions/labels provided by the user on the same training data set, sothat the re-trained face perception model can capture user's progressionor change in perception of desirable faces over time.

FIG. 2 presents a flowchart illustrating a process of processing faceimages using the constructed face perception model for the associatedindividual in accordance with some embodiments described herein. In someembodiments, the process begins by receiving new face images from one ormore sources (step 202). For example, the new face images can be profilephotos newly submitted/uploaded onto an online dating service or amobile dating App. However, if the individual/user is new to the onlinedating service or the mobile dating App, the new face images can be anyprofile photos in the profile photo database that have not previouslybeen processed on behalf of the individual/user. Next, the process makesinferences on behalf of the individual using the personalized DL faceperception model for the individual (e.g., which was trained using thedescribed model training process of FIG. 1) on the received new faceimages (step 204). For example, the personalized face perception modelcan generate desirability scores or make “like” or “dislike” decisionson behalf of the individual on the received new face images.

The process subsequently provides a subset of the set of new face imagesdetermined to be desirable or classified as “like” to the individual aspersonalized face image recommendations (step 206). For example, theprocess can classify a processed image as a desirable image if theinferred/determined desirability score by the personalized faceperception model for that image is above a predetermined threshold(e.g., 7 in a 1-10 scale). Note that the individual receiving therecommended face images from the proposed system can then rate therecommended face images by assigning desirability scores or making“like” or “dislike” decisions on these images. Because the size of therecommended face images can be quite small and manageable and the imagesare in compliance with the individual's preference/perception, theindividual receiving the recommended face images is highly likely toreact positively to the recommendations, e.g., by rating the recommendedface images. Next and optionally, the process can receive and store theindividual's ratings/decisions/selections on the recommended face imagesas new training data for re-training/updating the constructed faceperception model at a later time (step 208).

Note that the proposed desirability scores can be used to learn andquantify human emotional preferences and emotional states beyond thesimple “like” or “dislike” decisions. For example, when using thedisclosed face perception model training subsystem to train a faceperception model for a given user, the training face photos can be ratedwith a set of discrete levels (e.g., the desirability score with valuesfrom 1 to 10) and thus be classified into a set of ranking categoriesbased on the scores these training photos have received. Next, when thedisclosed face image processing subsystem is used to make inferences,each new face image/photo can be scored with one of the set of discretelevels (e.g., by selecting a discrete level having the highestprobability in the outputs) based on the leaned emotional preference ofthe given user. This ranking score becomes a numerical measure of thepreference level for each new photo of the given user predicted by theproposed face perception model.

The above-described automatic inference/ranking and scoring feature canhave significant implications. For example, it is possible to use atrained face perception model for a given user to rank any face image,not just the ones within an online dating system/App. More specifically,when a specific set of photos is scored by the trained face perceptionmodel for the given user, the generated ranking scores can revealinformation related to the user preference which was previously unknownto the user and can be extremely valuable for the proposed personalizedperception system. Moreover, each photo can be ranked and scored by agroup of trained face perception models for a group of users, andsubsequently an overall ranking score of that photo based on thecollective decisions of the group of users can be generated (e.g., bycomputing an average score). This overall ranking score representing anoverall preference level of the group of people can then be used forvarious advertising and marketing purpose.

Note that the above-described technique of using the proposedpersonalized face perception engine to automatically classify/rate/rankphotos to infer the preference of an individual and the preference ofgroups of people is not limited to face photos. The same technique canbe extended to other types of photos and images. For example, when thetechnique is used to process a photo of a particular fabric product(i.e., after personalized preference DL models on fabric images for alarge number of people have been established), the disclosed system andtechnique can be used to automatically infer preference levels on aparticular fabric product for individuals and/or for groups of people,without having to have the individuals or the group of people to look atthe photo.

FIG. 3 illustrates a block diagram of an exemplary personalized faceperception engine 300 for implementing the above-described personalizedface perception engine in accordance with some embodiments describedherein. As can be seen in FIG. 3, personalized face perception engine300 includes a face perception model training subsystem 302 coupled to aface image processing subsystem 304. Face perception model trainingsubsystem 302 further includes a DNN model 305. In some embodiments,face perception model training subsystem 302 receives a set of trainingface image 306 and a corresponding set of desirability scores 308provided by a person 310 (e.g., a paid user of an online dating service)on the set of training face image 306 based on person 310's perception,and subsequently uses the set of training face image 306 as inputs toDNN model 305 and the set of desirability scores 308 as targets/outputsof DNN model 305 to train DNN model 305 for person 310. Face perceptionmodel training subsystem 302 subsequently generates and outputs apersonalized face perception model 312, i.e., the uniquely trained DNNmodel 305 for person 310. In some embodiments, face image processingsubsystem 304 receives trained personalized face perception model 312and a set of new face images 314 from one or more sources, andautomatically processes the set of new face images 314 usingpersonalized face perception model 312 to infer desirability scores forthe set of new face images 314 and select a set of desirable face images316 among the set of new face images 314 on behalf of person 310 withextremely high accuracy. Face image processing subsystem 304subsequently provides a set of recommended profiles 318 based on the setof selected desirable face images 316 to person 310, which then makesfurther selections among the set of recommended profiles 318.

In some embodiments, at least a portion of personalized face perceptionengine 300 can be implemented on a deep-learning-specific hardwareplatform, including, but not limited to a graphic processing unit (GPU),a tensor processing unit (TPU), an intelligent processor unit (IPU), adigital signal processor (DSP), a field-programmable gate array (FPGA),and an application-specific integrated circuit (ASIC).

Note that the various embodiments of the above-described personalizedface perception engine, including personalized face perception engine300 in FIG. 3 can be implemented in various hardware environments. Insome embodiments, the disclosed face perception model training subsystemof the personalized face perception engine can be implemented on a cloudserver (e.g., Microsoft, Amazon, or Google cloud servers) whereas thedisclosed face image processing subsystem of the personalized faceperception engine can be implemented directly on terminal devices, suchas smart phones/tablets/laptops of the end users. In these embodiments,after the model training process, a constructed personalized faceperception model for a given user, such as face perception model 312 canbe downloaded from the cloud server to a terminal device of that user,such as a smartphone of person 310.

In some embodiments, the disclosed face perception model trainingsubsystem of the personalized face perception engine can be implementedon the servers of the online dating or mobile dating service providers,whereas the disclosed face image processing subsystem of thepersonalized face perception engine can be implemented directly onterminal devices of the end users. In these embodiments, after the modeltraining process, a constructed personalized face perception model for agiven user, such as face perception model 312 can be downloaded from amain server of a dating service provider to a terminal device of thatuser, such as a smartphone of person 310.

In some embodiments, both the disclosed face perception model trainingsubsystem and the disclosed face image processing subsystem of thepersonalized face perception engine can be implemented directly onterminal devices of the end users, provided that such terminal devicesare capable of performing required DL-based training and computations.According to these embodiments, the entire personalized face perceptionengine 300 can be implemented on a terminal device of person 310. Insome other embodiments, both the disclosed face perception modeltraining subsystem and the disclosed face image processing subsystem ofthe personalized face perception engine, such as personalized faceperception engine 300 can be implemented on a cloud server or on a mainserver of a dating service provider.

FIG. 4 illustrates an example network environment which provides forimplementing the disclosed personalized face perception engine inaccordance with some embodiments described herein. A network environment400 includes a number of electronic devices 402, 404 and 406communicably connected to a server 410 by a network 408. One or moreremote servers 420 are further coupled to the server 410 and/or the oneor more electronic devices 402, 404 and 406.

In some example embodiments, electronic devices 402, 404 and 406 can becomputing devices such as laptop or desktop computers, smartphones,PDAs, portable media players, tablet computers, televisions or otherdisplays with one or more processors coupled thereto or embeddedtherein, or other appropriate computing devices that can be used to fordisplaying a web page or web application. In one example, the electronicdevices 402, 404 and 406 store a user agent such as a browser orapplication. In the example of FIG. 4, electronic device 402 is depictedas a smartphone, electronic device 404 is depicted as a desktopcomputer, and electronic device 406 is depicted as a PDA.

Server 410 includes a processing device 412, which can include one ormore graphic processing units (GPUs), and a data store 414. Processingdevice 412 executes computer instructions stored in data store 414, forexample, for training, generating and updating a personalized faceperception model for a user of electronic devices 402, 404 and 406 foronline dating or mobile dating applications.

In some example aspects, server 410 can be a single computing devicesuch as a computer server. In other embodiments, server 410 canrepresent more than one computing device working together to perform theactions of a server computer (e.g., cloud computing). The server 410 mayhost the web server communicably coupled to the browser at the clientdevice (e.g., electronic devices 402, 404 or 406) via network 408. Inone example, the server 410 can be used to implement one or both of theface perception model training subsystem and the face image processingsubsystem of the disclosed personalized face perception engine inconjunction with FIGS. 1-3. Server 410 may further be in communicationwith one or more remote servers 420 either through the network 408 orthrough another network or communication means.

The one or more remote servers 420, which can include a cloud server,may perform various functionalities and/or storage capabilitiesdescribed herein with regard to the server 410 either alone or incombination with server 410. Each of the one or more remote servers 420may host various services. In one example, a remote cloud server 420 canbe used to implement one or both of the face perception model trainingsubsystem and the face image processing subsystem of the disclosedpersonalized face perception engine.

Server 410 may further maintain or be in communication with socialnetworking services hosted on one or more remote server 420. The one ormore social networking services may provide various services and mayenable users to create a profile and associate themselves with otherusers at a remote social networking service. The server 410 and/or theone or more remote servers 420 may further facilitate the generation andmaintenance of a social graph including the user created associations.The social graphs may include, for example, a list of all users of theremote social networking service and their associations with other usersof a remote social networking service.

Each of the one or more remote servers 420 can be a single computingdevice such as a computer server or can represent more than onecomputing device working together to perform the actions of a servercomputer (e.g., cloud computing). In one embodiment server 410 and oneor more remote servers 420 may be implemented as a single server oracross multiple servers. In one example, the server 410 and one or moreremote servers 420 may communicate through the user agent at the clientdevice (e.g., electronic devices 402, 404 or 406) via network 408.

Users may interact with the system hosted by server 410, and/or one ormore services hosted by remote servers 420, through a client applicationinstalled at the electronic devices 402, 404, and 406. Alternatively,the user may interact with the system and the one or more socialnetworking services through a web based browser application at theelectronic devices 402, 404, 406. Communication between client devices402, 404, 406 and the system, and/or one or more services, may befacilitated through a network (e.g., network 408).

Communications between the client devices 402, 404, 406, server 410and/or one or more remote servers 420 may be facilitated through variouscommunication protocols. In some aspects, client devices 402, 404, 406,server 410 and/or one or more remote servers 420 may communicatewirelessly through a communication interface (not shown), which mayinclude digital signal processing circuitry where necessary. Thecommunication interface may provide for communications under variousmodes or protocols, including Global System for Mobile communication(GSM) voice calls, Short Message Service (SMS), Enhanced MessagingService (EMS), or Multimedia Messaging Service (MMS) messaging, CodeDivision Multiple Access (CDMA), Time Division Multiple Access (TDMA),Personal Digital Cellular (PDC), Wideband Code Division Multiple Access(WCDMA), CDMA2000, or General Packet Radio System (GPRS), among others.For example, the communication may occur through a radio-frequencytransceiver (not shown). In addition, short-range communication mayoccur, including using a Bluetooth, WiFi, or other such transceiver.

The network 408 can include, for example, any one or more of a personalarea network (PAN), a local area network (LAN), a campus area network(CAN), a metropolitan area network (MAN), a wide area network (WAN), abroadband network (BBN), the Internet, and the like. Further, thenetwork 408 can include, but is not limited to, any one or more of thefollowing network topologies, including a bus network, a star network, aring network, a mesh network, a star-bus network, tree or hierarchicalnetwork, and the like.

A face perception engine which is configured to constructhighly-accurate and individualized face perception model for each uniqueindividual, wherein the constructed face perception model issubsequently used an agent of that individual for making desirabilitydecisions on face images is proposed. When making desirabilitydecisions, the constructed face perception model can be a substantiallyidentical representation of the given user. The model takes photo imagesof human faces as inputs, analyzes each photo in the associated deeplearning structure, and generates a “like” or “dislike” decision just asthat user would have made. A perfectly trained face perception modelshould behave exactly like the associated user him/herself, meaning themodel makes decisions on face images with nearly 100% likelihood as theuser him/herself.

The proposed face perception engine allows a user to visualize andbetter understand his/her personal preferences when it comes to faceattractiveness. The proposed face perception engine offers acost-effective way (“cost” includes time and money) to find a match(i.e., a date in dating services and Apps). The proposed face perceptionengine provides a natural way of generating data of user preferences,wherein the data can include those features that are very difficult orimpossible to describe, and therefore not collected in the past.

In addition to the above-described application in dating services andApps, the proposed face perception engine also provides a mechanism forconstructing highly individualized models and agents for other highlypersonal applications including, but not limited to content delivery,targeted advertisement, and building personal entertainment agents. Eachapplication model can be constructed with very high accuracy for theassociated individual because the model is constructed primarily basedon the knowledge of that specific individual.

These functions described above can be implemented in digital electroniccircuitry, in computer software, firmware or hardware. The techniquescan be implemented using one or more computer program products.Programmable processors and computers can be included in or packaged asmobile devices. The processes and logic flows can be performed by one ormore programmable processors and by one or more programmable logiccircuitry. General and special purpose computing devices and storagedevices can be interconnected through communication networks.

While this patent document contains many specifics, these should not beconstrued as limitations on the scope of any invention or of what may beclaimed, but rather as descriptions of features that may be specific toparticular embodiments of particular inventions. Certain features thatare described in this patent document in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults.

Only a few implementations and examples are described and otherimplementations, enhancements and variations can be made based on whatis described and illustrated in this patent document.

What is claimed is:
 1. A computer-implemented method of constructing apersonalized face perception model for a unique individual, the methodcomprising: receiving a set of face images and a corresponding set ofdesirability scores, wherein each of the desirability scores representsa degree of desirability toward an associated face image provided by theindividual based on the individual's perception of a desirable face;providing the set of face images and the corresponding set ofdesirability scores to a deep learning (DL) neural network, wherein theDL neural network includes a set of features and a set of parametersassociated with the set of features; and training the DL neural networkusing the set of face images as inputs and the corresponding set ofdesirability scores as outputs to generate a personalized faceperception model for the unique individual, wherein the personalizedface perception model includes a trained set of parameters which isunique to the unique individual, wherein the personalized faceperception model is used to automatically infer a desirability scorefrom a new face image on behalf of the unique individual according tothe learned perception of the unique individual.
 2. Thecomputer-implemented method of claim 1, wherein prior to receiving theset of face images and the corresponding set of desirability scores, themethod further comprises generating the corresponding set ofdesirability scores by: providing the set of face images to the uniqueindividual; and guiding the unique individual to label each of the setof face images with a desirability score based on the uniqueindividual's inherent ability of judging a face as desirable orundesirable.
 3. The computer-implemented method of claim 1, wherein thedesirability score for a given face image in the set of face images isone of a set of discrete values representing different degrees ofdesirability toward the given face image.
 4. The computer-implementedmethod of claim 1, wherein for a particular group of individuals, theset of face images is substantially identical for each individual in thegroup of individuals, while the set of desirability scores is differentfor different individuals in the group of individuals.
 5. Thecomputer-implemented method of claim 1, wherein the DL neural networkincludes a convolution neural network (CNN), and wherein the set offeatures includes a set of pre-defined filters representing a set ofpre-defined facial features, and wherein training the CNN using the setof face images includes training a set of weights associated with theset of pre-defined facial features.
 6. The computer-implemented methodof claim 5, wherein the set of features additionally includes a set ofunspecified filters, wherein each of the set of unspecified filtersincludes a set of trainable parameters, and wherein training the CNNusing the set of face images additionally includes training the set oftrainable parameters in the set unspecified filters to construct the setunspecified filters for the unique individual.
 7. Thecomputer-implemented method of claim 1, wherein after generating thepersonalized face perception model, the method further comprisesapplying the personalized face perception model to a large number of newface images to select desirable face images among the new face images onbehalf of the unique individual with very high accuracy, therebypreventing the unique individual from personally screening the largenumber of new face images.
 8. The computer-implemented method of claim7, wherein applying the personalized face perception model to the largenumber of new face images includes: receiving each new face image as aninput to the personalized face perception model; generating alike/dislike decision or a desirability score for the new face image onbehalf of the unique individual using the trained DL neural network; andif the new face image is determined with a like decision or to bedesirable, providing the new face image to the unique individual as apersonalized recommendation.
 9. The computer-implemented method of claim8, wherein the method further comprises: receiving a user decision onthe recommended new face image from the unique individual; and updatingthe personalized face perception model using the new face image and theassociated user decision as a part of new training data.
 10. Thecomputer-implemented method of claim 8, wherein the method furthercomprises: generating a plurality of personalized face perception modelsfor a plurality of individuals, wherein each of the plurality ofpersonalized face perception models corresponds to each of the pluralityof individuals, and wherein each of the plurality of personalized faceperception models generates a desirability score for an input face imageon behalf of the corresponding individual, and wherein the desirabilityscore has a value from a set of discrete values representing differentdegrees of desirability; applying the plurality of personalized faceperception models to a given face image to generate a set ofdesirability scores for the plurality of individuals; and computing anoverall desirability score for the given face image by averaging the setof desirability scores, wherein the overall desirability score measuresan overall degree of desirability for the given face image of theplurality of individuals.
 11. A personalized face perception system,comprising: one or more processors; a memory coupled to the one or moreprocessors; a face perception model training subsystem including a deeplearning (DL) neural network, wherein the DL neural network includes aset of features and a set of parameters associated with the set offeatures, wherein the face perception model training subsystem isconfigured to: receive a set of face images and a corresponding set ofdesirability scores, wherein each of the desirability scores representsa degree of desirability toward an associated face image provided by anindividual based on the individual's perception of a desirable face; andtrain the DL neural network using the set of face images as inputs tothe DL neural network and the corresponding set of desirability scoresas outputs of the DL neural network to generate a personalized faceperception model for the individual, wherein the personalized faceperception model includes a trained set of parameters which is unique tothe individual; and a face image processing subsystem coupled to theface perception model training subsystem, wherein the face imageprocessing subsystem is configured to: receive the personalized faceperception model from the face perception model training subsystem;receive a set of new face images from an external source; and applyingthe personalized face perception model to the set of new face images toselect desirable face images among the set of new face images on behalfof the individual with very high accuracy, thereby preventing theindividual from personally screening the set of new face images.
 12. Thepersonalized face perception system of claim 11, wherein prior toreceiving the set of face images and the corresponding set ofdesirability scores, the face perception model training subsystem isfurther configured to generate the corresponding set of desirabilityscores by: providing the set of face images to the unique individual;and guiding the unique individual to label each of the set of faceimages with a desirability score based on the unique individual'sinherent ability of judging a face as desirable or undesirable.
 13. Thepersonalized face perception system of claim 11, wherein thedesirability score for a given face image in the set of face images isone of a set of discrete values representing different degrees ofdesirability toward the given face image.
 14. The personalized faceperception system of claim 11, wherein the DL neural network includes aconvolution neural network (CNN), and wherein the set of featuresincludes a set of pre-defined filters representing a set of pre-definedfacial features, and wherein training the CNN using the set of faceimages includes training a set of weights associated with the set ofpre-defined facial features.
 15. The personalized face perception systemof claim 14, wherein the set of features additionally includes a set ofunspecified filters, wherein each of the set of unspecified filtersincludes a set of trainable parameters, and wherein training the CNNusing the set of face images additionally includes training the set oftrainable parameters in the set unspecified filters to construct the setunspecified filters for the unique individual.
 16. The personalized faceperception system of claim 11, wherein the face image processingsubsystem is configured to apply the personalized face perception modelto a large number of new face images to select desirable face imagesamong the new face images on behalf of the unique individual with veryhigh accuracy, thereby preventing the unique individual from personallyscreening the large number of new face images.
 17. The personalized faceperception system of claim 16, wherein the face image processingsubsystem is configured to apply the personalized face perception modelto the large number of new face images by: receiving each new face imageas an input; generating a like/dislike decision or a desirability scorefor the new face image on behalf of the unique individual using thetrained DL neural network; and if the new face image is determined witha like decision or to be desirable, providing the new face image to theunique individual as a personalized recommendation.
 18. The personalizedface perception system of claim 17, wherein the face perception modeltraining subsystem is further configured to: receive a user decision onthe recommended new face image from the unique individual; and updatethe personalized face perception model using the new face image and theassociated user decision as a part of new training data.
 19. Thepersonalized face perception system of claim 17, wherein the faceperception model training subsystem is further configured to: generate aplurality of personalized face perception models for a plurality ofindividuals, wherein each of the plurality of personalized faceperception models corresponds to each of the plurality of individuals,and wherein each of the plurality of personalized face perception modelsgenerates a desirability score for an input face image on behalf of thecorresponding individual, and wherein the desirability score has a valuefrom a set of discrete values representing different degrees ofdesirability; and wherein the face image processing subsystem is furtherconfigured to: apply the plurality of personalized face perceptionmodels to a given face image to generate a set of desirability scoresfor the plurality of individuals; and compute an overall desirabilityscore for the given face image by averaging the set of desirabilityscores, wherein the overall desirability score measures an overalldegree of desirability for the given face image of the plurality ofindividuals.
 20. A computer-implemented method of constructing and usinga personalized face perception model for a unique individual, the methodcomprising: receiving a set of face images and a corresponding set ofdesirability scores, wherein each of the desirability scores representsa degree of desirability toward an associated face image provided by anindividual based on the individual's perception of a desirable face;receiving a personalized face perception model based on a deep learning(DL) neural network, wherein the DL neural network includes a set offeatures and a set of parameters associated with the set of features;training the personalized face perception model using the set of faceimages as inputs to the DL neural network and the corresponding set ofdesirability scores as outputs of the DL neural network to generate atrained personalized face perception model for the individual, whereinthe trained personalized face perception model includes a trained set ofparameters which is unique to the individual; and applying the trainedpersonalized face perception model to a set of new face images to selectdesirable face images among the set of new face images on behalf of theindividual with very high accuracy, thereby preventing the individualfrom personally screening the set of new face images.